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Salt stress profoundly affects plant growth, prompting intricate molecular responses, such as alternative splicing (AS), for environmental adaptation. However, the response of AS events to salt stress in Opisthopappus (Opisthopappus taihangensis and Opisthopappus longilobus) remains unclear, which is a Taihang Mountain cliff-dwelling species. Using RNA-seq data, differentially expressed genes (DEGs) were identified under time and concentration gradients of salt stress. Two types of AS, skipped exon (SE) and mutually exclusive exons (MXE), were found. Differentially alternative splicing (DAS) genes in both species were significantly enriched in “protein phosphorylation”, “starch and sucrose metabolism”, and “plant hormone signal transduction” pathways. Meanwhile, distinct GO terms and KEGG pathways of DAS occurred between two species. Only a small subset of DAS genes overlapped with DEGs under salt stress. Although both species likely adopted protein phosphorylation to enhance salt stress tolerance, they exhibited distinct responses. The results indicated that the salt stress mechanisms of both Opisthopappus species exhibited similarities and differences in response to salt stress, which suggested that adaptive divergence might have occurred between them. This study initially provides a comprehensive description of salt responsive AS events in Opisthopappus and conveys some insights into the molecular mechanisms behind species tolerance on the Taihang Mountains.

Actinobacillus pleuropneumoniae (APP) is the causative pathogen of porcine pleuropneumonia, a highly contagious respiratory disease in the pig industry. The increasingly severe antimicrobial resistance in APP urgently requires novel antibacterial alternatives for the treatment of APP infection. In this study, we investigated the effect of tea polyphenols (TP) against APP. MIC and MBC of TP showed significant inhibitory effects on bacteria growth and caused cellular damage to APP. Furthermore, TP decreased adherent activity of APP to the newborn pig tracheal epithelial cells (NPTr) and the destruction of the tight adherence junction proteins β-catenin and occludin. Moreover, TP improved the survival rate of APP infected mice but also attenuated the release of the inflammation-related cytokines IL-6, IL-8, and TNF-α. TP inhibited activation of the TLR/MAPK/PKC-MLCK signaling for down-regulated TLR-2, TLR4, p-JNK, p-p38, p-PKC-α, and MLCK in cells triggered by APP. Collectively, our data suggest that TP represents a promising therapeutic agent in the treatment of APP infection.

Streptococcus suis (S. suis) is one of the most important zoonotic pathogens that threaten the lives of pigs and humans. Even worse, the increasingly severe antimicrobial resistance in S. suis is becoming a global issue. Therefore, there is an urgent need to discover novel antibacterial alternatives for the treatment of S. suis infection. In this study, we investigated theaflavin (TF1), a benzoaphenone compound extracted from black tea, as a potential phytochemical compound against S. suis. TF1 at MIC showed significant inhibitory effects on S. suis growth, hemolytic activity, and biofilm formation, and caused damage to S. suis cells in vitro. TF1 had no cytotoxicity and decreased adherent activity of S. suis to the epithelial cell Nptr. Furthermore, TF1 not only improved the survival rate of S. suis-infected mice but also reduced the bacterial load and the production of IL-6 and TNF-α. A hemolysis test revealed the direct interaction between TF1 and Sly, while molecular docking showed TF1 had a good binding activity with the Glu198, Lys190, Asp111, and Ser374 of Sly. Moreover, virulence-related genes were downregulated in the TF1-treated group. Collectively, our findings suggested that TF1 can be used as a potential inhibitor for treating S. suis infection in view of its antibacterial and antihemolytic activity.

Alfalfa growth and production in China are negatively impacted by high salt concentrations in soils, especially in regions with limited water supplies. Few reliable genetic markers are currently available for salt tolerance selection. As a result, molecular breeding strategies targeting alfalfa are hindered. Therefore, with the continuous increase in soil salinity in agricultural lands, it is indispensable that a salt-tolerant variety of alfalfa is produced. We collected 220 alfalfa varieties around the world for resequencing and performed genome-wide association studies (GWASs). Alfalfa seeds were germinated in saline water with different concentrations of NaCl, and the phenotypic differences in several key root traits were recorded. In the phenotypic analysis, the breeding status and geographical origin strongly affected the salt tolerance of alfalfa. Forty-nine markers were significantly associated with salt tolerance, and 103 candidate genes were identified based on linkage disequilibrium. A total of 2712 differentially expressed genes were upregulated and 3570 were downregulated based on transcriptomic analyses. Some candidate genes that affected root development in the seed germination stage were identified through the combination of GWASs and transcriptome analyses. These genes could be used for molecular breeding strategies to increase alfalfa’s salt tolerance and for further research on salt tolerance in general.

Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than soluble proteins. The topology of transmembrane proteins (TMPs) can determine the spatial conformation relative to the membrane, while the secondary structure helps to identify their functional domain. They are highly correlated on αTMPs sequences, and achieving a merge prediction is instructive for further understanding the structure and function of αTMPs. In this study, we implemented a hybrid model combining Deep Learning Neural Networks (DNNs) with a Class Hidden Markov Model (CHMM), namely HDNNtopss. DNNs extract rich contextual features through stacked attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs), and CHMM captures state-associative temporal features. The hybrid model not only reasonably considers the probability of the state path but also has a fitting and feature-extraction capability for deep learning, which enables flexible prediction and makes the resulting sequence more biologically meaningful. It outperforms current advanced merge-prediction methods with a Q4 of 0.779 and an MCC of 0.673 on the independent test dataset, which have practical, solid significance. In comparison to advanced prediction methods for topological and secondary structures, it achieves the highest topology prediction with a Q2 of 0.884, which has a strong comprehensive performance. At the same time, we implemented a joint training method, Co-HDNNtopss, and achieved a good performance to provide an important reference for similar hybrid-model training.

Radical cure colitis is a severe public health threat worldwide. Our previous studies have confirmed that melatonin can effectively improve gut microbiota disorder and mucosal injury caused by sleep deprivation (SD). The present study further explored the mechanism whereby exogenous melatonin prevented SD-induced colitis. 16S rRNA high-throughput sequencing and metabolomics analysis were used to explore the correlation between SD-induced colitis and intestinal microbiota and metabolite composition in mice. Fecal microbiota transplantation (FMT) and melatonin or butyrate supplementation tests verified the core role of gut microbiota in melatonin-alleviating SD-induced colitis. Further, in vitro tests studied the modulatory mechanism of metabolite butyrate. The results demonstrated that SD leads to reductions in plasma melatonin levels and colonic Card9 expression and consequent occurrence of colitis and gut microbiota disorder, especially the downregulation of Faecalibacterium and butyrate levels. The FMT from SD-mice to normal mice could restore SD-like colitis, while butyrate supplementation to SD-mice inhibited the occurrence of colitis, but with no change in the plasma melatonin level in both treatments. However, melatonin supplementation reversed all inductions in SD-mice. In intestinal epithelial cells, the inflammatory ameliorative effect of butyrate was blocked with pretreatments of HDAC3 agonist and HIF-1α antagonist but was mimicked by GSK-3β and p-P65 antagonists. Therefore, the administration of MLT may be a better therapy for SD-induced colitis relative to butyrate. A feasible mechanism would involve that melatonin up-regulated the Faecalibacterium population and production of its metabolite butyrate and MCT1 expression and inhibited HDAC3 in the colon, which would allow p-GSK-3β/β-catenin/HIF-1α activation and NF-κB/NLRP3 suppression to up-regulate Card9 expression and suppress inflammation response.

Scutellaria baicalensis is a well-known medicinal plant that produces biologically active flavonoids, such as baicalin, baicalein, and wogonin. Pharmacological studies have shown that these compounds have anti-inflammatory, anti-bacterial, and anti-cancer activities. Therefore, it is of great significance to investigate the genetic information of S. baicalensis, particularly the genes related to the biosynthetic pathways of these compounds. Here, we constructed the full-length transcriptome of S. baicalensis using a hybrid sequencing strategy and acquired 338,136 full-length sequences, accounting for 93.3% of the total reads. After the removal of redundancy and correction with Illumina short reads, 75,785 nonredundant transcripts were generated, among which approximately 98% were annotated with significant hits in the protein databases, and 11,135 sequences were classified as lncRNAs. Differentially expressed gene (DEG) analysis showed that most of the genes related to flavonoid biosynthesis were highly expressed in the roots, consistent with previous reports that the flavonoids were mainly synthesized and accumulated in the roots of S. baicalensis. By constructing unique transcription models, a total of 44,071 alternative splicing (AS) events were identified, with intron retention (IR) accounting for the highest proportion (44.5%). A total of 94 AS events were present in five key genes related to flavonoid biosynthesis, suggesting that AS may play important roles in the regulation of flavonoid biosynthesis in S. baicalensis. This study provided a large number of highly accurate full-length transcripts, which represents a valuable genetic resource for further research of the molecular biology of S. baicalensis, such as the development, breeding, and biosynthesis of active ingredients.

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol−1) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol−1 to 0.15 and 0.18 kcal·mol−1, respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol−1. This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules.

We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond dissociation energies (BDE) of Y-NO bond. As a demonstration, this combined quantum chemistry calculation with the GP-GRNN approach has been applied to evaluate the homolysis BDE of 92 Y-NO organic molecules. The results show that the full-descriptor GRNN without GRA and PCA (F-GRNN) and with GRA (G-GRNN) approaches reduce the root-mean-square (RMS) of the calculated homolysis BDE of 92 organic molecules from 5.31 to 0.49 and 0.39 kcal mol−1 for the B3LYP/6-31G (d) calculation. Then the newly developed GP-GRNN approach further reduces the RMS to 0.31 kcal mol−1. Thus, the GP-GRNN correction on top of B3LYP/6-31G (d) can improve the accuracy of calculating the homolysis BDE in quantum chemistry and can predict homolysis BDE which cannot be obtained experimentally.